The indicator ‘CO2 equivalents emissions’ is one of the most prominent sustainability parameters. It reflects the importance of climate change as a key issue in the overall energy debate. It should be noted at this point that, even though renewable energy is a viable means for reducing carbon emissions and mitigating climate change, renewable energy technologies are not CO2 neutral: their production and the installation of infrastructure require energy inputs in form of fossil fuels (Pehnt 2006). Nevertheless, renewable energy provision in general is associated with fewer emissions when compared to conventional, fossil fuel based energy provision. For example, even modern fossil fuel based energy generation, based on a co-generation gas turbine) is associated with CO2 equivalents-emissions of 158.6 t/ TJ during the entire life cycle, whereas renewable energy technologies, according to GEMIS (Ökologie-Institut 2006), range between 0 and 84 t/ TJ CO2 equivalents.
It is sensible to consider the associated CO2 emissions of renewable energy technologies, as it is the net-effect of reducing GHG emissions to the atmosphere that is of importance. The substitution potential for fossil fuels has to be reduced by GHG emissions that accrue during the establishment and operation of renewable energy technologies.
In particular the share of biomass-based energy provision has a large impact on the GHG performance of the scenarios. The GEMIS database (Ökologie-Institut 2006) indicates that
biomass for energy provision, in particular for non-heat forms, is associated with especially large emissions per unit of energy provided. These emissions occur during the transport, fertilisation, drying and in some cases gasification processes.
The scenarios A-E are all similar according to their CO2 emissions, ranging from 16 to 21 tCO2
equivalents /TJ/a. Scenario D, the biomass scenario, clearly shows larger emissions (21t CO2 equivalents/TJ/a). Since the scenarios comprise only parts of the Austrian energy supply systems, and focus on electricity and heat generation only, it is not possible to compare CO2
results with the other studies available in Austria for 2020 (see above). A comparison with partial results of existing studies reveals that the results are well in line with other scenario analyses.
According to the impacts on air quality which are approximated by the indicators SO2 equivalents, TOPP (surface near ozone) and particulate matter, the overall picture shows that in a life cycle perspective renewables perform much better than fossil fuels. However, scenarios are different due to the different mix of renewable technologies applied which all show different profiles with regard to these indicators. Biomass utilisation, which always includes a combustion process and therefore air emissions, is associated with large emissions. In particular, the emission of particulate matter is relevant for biomass combustion processes. (Baumbach et al. 2010) This causes the weak performance of scenario D. On the other hand, small scale technologies are characterised by comparably larger air emissions, owing to difficulties in reaching high efficiency standards which require installation of costly and sophisticated filter systems. A sensitivity analysis that investigates the specific effects of the degree of decentralisation of future energy systems clearly illustrates that fostering decentralised energy systems results in severe rises with regard to these environmental indicators (for further detail see section Sensitivity Analysis). The scenarios C and E, in contrast, are of a more large-scale nature with high systems efficiency and a high share of cascade utilisation. In consequence, it is no surprise that these scenarios perform well with regard to these environmental criteria.
Scenario E always ranks lower than scenario C, because the negative environmental impacts of decentralised technologies outweigh the positive environmental effects of cascade utilisation.
The criteria Ecological Justice was operationalised by the indicator Sealed Area Equivalents (see Method section). This indicator is based on the basic accounting principle of the sustainability indicator Human Appropriation of Net Primary Production (HANPP; Vitousek 1986, Haberl and Erb Haberl 1997 Haberl et al. 2004b, Erb et al. 2009), The indicator is an estimate of the ecological pressure associated with the operation of renewable technologies, resulting from instalment of production plants and also the harvest of biomass. It estimates the
amount of area that would have to be sealed – and thus deprived of vegetation – in order to exert the same pressure as the sum of installation and biomass harvest (see Method section). Thus, this indicator can be interpreted as a proxy for the appropriation of biological production by human society which, in consequence, is not available any more for other heterotrophic species.
This indicator is specifically sensitive to the extent of primary biomass use, as the biomass harvest impact is by far larger than the impact on production exerted by the installation of power plants. Only a large share of hydropower, which itself has a disproportionately high sealed area demand is also relevant in this context. In other words, the area required to install land-based infrastructure is much smaller than the area required for harvesting the required amounts of biomass. This finding is well in line with the recent study by Haberl and colleagues (Haberl et al. 2007), that found that from the total of 24% of the global human appropriation of net primary production in the year 2000, only 4% are due to infrastructure, the lion’s share (50%) being due to agricultural activities. The differences of the individual scenarios related to the criterion Ecological Justice are similar to most other indicators of the environmental evaluation dimension: scenario D performs poorly and scenario C performs well. Sealed area equivalents are proportionally linked to the use of primary biomass and are insensitive to the use of biomass residues, a fact that is also demonstrated clearly in the sensitivity analysis. Those scenarios with large-scale photo voltaic energy generation perform particularly, as it is assumed that PV cells are only installed on rooftops and facades of buildings and are thus not accounted for in the sealed area equivalent calculation.
The indicators Cumulated Energy Effort and Cumulated Material Effort are highly aggregated environmental pressure indicators with complex definitions of system boundaries (Haberl Haberl et al. 2004a, Haberl 2001, . They can be understood as indicators of overall environmental burdens exerted by certain activities. However, it is difficult to associate these flows directly and unambiguously with their direct environmental impacts (van der Voet et al.
2004), which sometimes hampers straightforward interpretation of the results. Nevertheless, it is commonly agreed in the literature (Baccini et al. 1991, Adriaanse et al. 1997, Weisz and Duchin 2006; Weisz and Schandl 2008, Matthews et al. 2000) that these indicators are particularly indicative for the analysis of trends, and, in combination with economic indicators, are able to give evidence for relative and absolute decoupling, i.e. the de-linking of economic value added from resource use. This is the reason why the stakeholder in the participatory process have agreed to use this indicator set for indicating environmental burdens beyond climate change, air quality and ecological justice. Cumulated Material Effort is particularly large in scenario C, with 105.2 t per TJ/a, which is a result of the material intensive silicate mining processes associated to the production of photovoltaic (PV) cells. All other scenarios range between 75.5 and 83.2 t /TJ/a Despite the energy intensive production process of PV, scenario C is much less
pronounced when calculating cumulative energy flows, where scenario C performs best. A possible explanation might be that even PV technology production processes are rather energy intensive; the running of the technology might be counterbalancing this effect. Overall, PV offers good performance according the cumulative energy flows. However, this might indicate data distortion, owing to insufficient input data in the GEMIS database. Thus, further analysis of this indicator will have to be handled with care.
One of the most central social sustainability criteria, social justice, could not be applied in the appraisal, because – as a result of the interviews with the experts – it became clear that degrees of Social Justice can not be judged from the information generated in the scenario modelling.
For example, an operationalisation of social justice could be the affordability of energy;
however, information was not available to make a solid estimation of energy prices. To make robust statements on prices and affordability in 2020, assumptions on concrete policy measures and subsidy schemes are necessary, which was far beyond the scope of the empirical effort presented here. For that reason the indictor was eliminated from the assessment. Nevertheless, the criterion was included in the stakeholder ranking exercise and is therefore part of the discussion on the importance of sustainability criteria for more sustainable energy systems.
Naturally, there are also economic reasons in favour of installing renewable energy technologies in general. (Volpi 2005) Many renewables have the structural economic advantage that the variable costs are very low. There might be manifest investment costs but usually the running of the renewable power or heat plant is decisively cheaper than fossil fuel power plants. (Jacobsson and Lauber 2005) Governmental subsidies schemes provide starting aid for technologies not fully ready for competitive markets87. The costs of renewable energy technologies do, however, vary. The overall picture according to the renewable energy scenarios at hand is that the two decentralised scenarios E and C are associated with higher costs per energy unit. This is not further surprising. Scenario C ranks lowest in the cost factor, i.e. it is associated with the highest costs, being characterised by small scale technologies with large infrastructure costs and photovoltaics as the key technology. Scenario D, the biomass strategy, is performing best according to the cost factor, whereas the scenarios with large scale technology (A and B) yield similar results (between 40,000 and 43,000 €/ TJ).
87 Which indeed as been the same case at the outset of fossil-fuel technology, as well as nuclear power and large-scale hydro power. (Jacobsson and Lauber 2005)
The indicator ‘Effect on the Public Budget’ represents the macro economic costs dimension integrated in the sustainability appraisal. This indicator does not account for public revenue but only for public spending. Whereas the criterion ‘Costs’ addresses the microeconomic costs on the business level, the criterion ‘Effect on the public budget’ focuses on the public expenditures.
Governmental subsidies are investments covered by taxes that allow for a certain time pilot technologies to develop in a niche until they are able to gain market competitiveness. 88 In consequence, mature, established technologies perform better then young niche technologies. In the scenario appraisal, a further assumption was influencing this indicator: the establishment of new institutions is associated with surging increases in the public budget. New institutions represent an additional policy effort and consequently an enhanced public budget spending. This is surely a strong oversimplification that does not take into account the fact that new institutions might be more efficient and less prone to e.g. organisational problems and conflicting spheres of interest. Nevertheless, this reasoning is an attempt to address the institutional change dimension in the appraisal also from an economic point of view.
The other prominent macroeconomic dimension, employment, is discussed widely in context of renewable energy technologies. In the year 2004 19,100 jobs were available in the renewable technology production sector in Austria, and an additional 13,600 jobs were created in servicing and running renewable energy technologies. (Haas et al. 2006) This is quite a substantial contribution to the Austrian labour market. Employment has a social and an economic side:
specifically, for the employment of (part time) farmers and in general for the benefit of inhabitants of rural areas. Employment represents an important political, social and economic challenge that can be addressed by the establishment and expansion of renewable energy systems. Among the renewable energy scenarios the greatest effect on employment is attributed to scenario E, owing to the decentralised nature of the energy system. Generally speaking, decentralised technologies are more labour intensive per energy unit than centralised, large scale technologies. However, the specific quality of the generated jobs is not addressed in the appraisal, which is an important aspect, in particular with regard to gender issues (Abbasi and Abbasi 2000, Clancy et al. 2004). Looking at it from a labour market perspective, though, there
88 The economic gains due to renewable energy technologies are substantial in Austria but not included in this appraisal. Recent studies show that the production of renewable energy technologies created a business volume of € 1,461 Mio Euro in Austria in 2004. The study accounts for direct effects (technology production), indirect effects (intermediate products, such as semiconductor industry in Austria) and secondary effects (due to increased incomes). (Haas et al. 2006)
exist more important and also more efficient employment policy tools that operate towards full employment than there is for management of energy transitions.
The spider web diagrams clearly show the overall sustainability profile of the scenarios. A comparison of the scenarios in the area size show significant differences, and the favourable scenarios (E and C) are well indicated. However, the diagram also shows that the scenarios have top performance in certain sustainability appraisal criteria. E.g. scenario D, which is obviously not performing very well overall, is the best according to the ‘Costs’ criterion and certain Water quality indicators (inorganic acids, AOX).